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Features Importance (Original Scale)

Scaled Features Importance (MinMax per Model)

Spearman Correlation of Models

Summary of 4_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.5 seconds
Metric details:
| Metric |
Score |
| MAE |
132.784 |
| MSE |
29172.8 |
| RMSE |
170.801 |
| R2 |
0.620462 |
| MAPE |
0.19442 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 18_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.05
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.1 seconds
Metric details:
| Metric |
Score |
| MAE |
132.488 |
| MSE |
29059.1 |
| RMSE |
170.467 |
| R2 |
0.621941 |
| MAPE |
0.194198 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 17_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 0.9
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.1 seconds
Metric details:
| Metric |
Score |
| MAE |
132.061 |
| MSE |
28908.3 |
| RMSE |
170.025 |
| R2 |
0.623903 |
| MAPE |
0.193339 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 16_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.9
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.5 seconds
Metric details:
| Metric |
Score |
| MAE |
132.06 |
| MSE |
28893.4 |
| RMSE |
169.981 |
| R2 |
0.624097 |
| MAPE |
0.193587 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 14_CatBoost |
2 |
| 16_CatBoost |
1 |
| 17_CatBoost |
1 |
| 19_Xgboost |
2 |
| 20_NeuralNetwork |
2 |
| 3_Default_CatBoost |
4 |
| 4_Default_NeuralNetwork |
1 |
Metric details:
| Metric |
Score |
| MAE |
131.979 |
| MSE |
28862.7 |
| RMSE |
169.89 |
| R2 |
0.624496 |
| MAPE |
0.19343 |
Learning curves

True vs Predicted

Predicted vs Residuals

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Summary of 19_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
1.6 seconds
Metric details:
| Metric |
Score |
| MAE |
132.481 |
| MSE |
29051.4 |
| RMSE |
170.445 |
| R2 |
0.622041 |
| MAPE |
0.19421 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 21_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.08
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.8 seconds
Metric details:
| Metric |
Score |
| MAE |
133.129 |
| MSE |
29324.9 |
| RMSE |
171.245 |
| R2 |
0.618484 |
| MAPE |
0.194396 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 10_LightGBM
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LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 15
- learning_rate: 0.05
- feature_fraction: 0.8
- bagging_fraction: 0.5
- min_data_in_leaf: 50
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.7 seconds
Metric details:
| Metric |
Score |
| MAE |
134.194 |
| MSE |
29717.5 |
| RMSE |
172.388 |
| R2 |
0.613375 |
| MAPE |
0.196031 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 3_Default_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
1.7 seconds
Metric details:
| Metric |
Score |
| MAE |
132.054 |
| MSE |
28892.7 |
| RMSE |
169.979 |
| R2 |
0.624106 |
| MAPE |
0.19349 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 20_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.1 seconds
Metric details:
| Metric |
Score |
| MAE |
132.387 |
| MSE |
29037.8 |
| RMSE |
170.405 |
| R2 |
0.622218 |
| MAPE |
0.193753 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 2_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
1.8 seconds
Metric details:
| Metric |
Score |
| MAE |
132.51 |
| MSE |
29059.5 |
| RMSE |
170.468 |
| R2 |
0.621936 |
| MAPE |
0.194258 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 14_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.4 seconds
Metric details:
| Metric |
Score |
| MAE |
132.021 |
| MSE |
28890.9 |
| RMSE |
169.973 |
| R2 |
0.62413 |
| MAPE |
0.193452 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 15_CatBoost
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CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.7
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
9.4 seconds
Metric details:
| Metric |
Score |
| MAE |
132.098 |
| MSE |
28905 |
| RMSE |
170.015 |
| R2 |
0.623946 |
| MAPE |
0.193472 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 6_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 8
- min_child_weight: 5
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
1.9 seconds
Metric details:
| Metric |
Score |
| MAE |
133.423 |
| MSE |
29422.3 |
| RMSE |
171.529 |
| R2 |
0.617216 |
| MAPE |
0.195553 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 5_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.5 seconds
Metric details:
| Metric |
Score |
| MAE |
134.307 |
| MSE |
29686.6 |
| RMSE |
172.298 |
| R2 |
0.613777 |
| MAPE |
0.195516 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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Summary of 1_Default_LightGBM
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LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.1 seconds
Metric details:
| Metric |
Score |
| MAE |
132.86 |
| MSE |
29205.6 |
| RMSE |
170.896 |
| R2 |
0.620035 |
| MAPE |
0.194671 |
Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

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